CEI：一种用于3D空间跨身体视觉运动策略学习的统一接口
CEI: A Unified Interface for Cross-Embodiment Visuomotor Policy Learning in 3D Space
AI总结：
CEI提出了一种跨身体视觉运动策略学习框架，通过功能相似性量化和梯度优化实现不同机械臂和末端执行器间的演示转移，实验显示在模拟和现实任务中均取得高转移效率。
摘要：
机器人基础模型在大规模操作数据集上训练后，显示出学习通用策略的潜力，但它们常常过度适应特定视角、机械臂和尤其是平行 jaw 夹具，这是由于数据集偏差所致。为了解决这一限制，我们提出了跨身体接口（CEI），一种用于跨身体学习的框架，使演示能够在不同机械臂和末端执行器形态之间进行转移。CEI引入了功能相似性的概念，该概念通过方向偏差距离量化。然后通过基于梯度的优化对齐机器人轨迹，随后为未见过的机器人臂和末端执行器合成观测和动作。在实验中，CEI将数据和策略从Franka Panda机器人转移到3个任务中的16种不同身体，且在现实世界中支持UR5+AG95夹具机器人与UR5+Xhand机器人之间6个任务的双向转移，实现了平均转移比为82.4%。最后，我们证明CEI也可以通过我们提出的技术扩展出空间泛化和多模态运动生成能力。项目网站：https://cross-embodiment-interface.github.io/
英文摘要：
Robotic foundation models trained on large-scale manipulation datasets have shown promise in learning generalist policies, but they often overfit to specific viewpoints, robot arms, and especially parallel-jaw grippers due to dataset biases. To address this limitation, we propose Cross-Embodiment Interface (\CEI), a framework for cross-embodiment learning that enables the transfer of demonstrations across different robot arm and end-effector morphologies. \CEI introduces the concept of \textit{functional similarity}, which is quantified using Directional Chamfer Distance. Then it aligns robot trajectories through gradient-based optimization, followed by synthesizing observations and actions for unseen robot arms and end-effectors. In experiments, \CEI transfers data and policies from a Franka Panda robot to \textbf{16} different embodiments across \textbf{3} tasks in simulation, and supports bidirectional transfer between a UR5+AG95 gripper robot and a UR5+Xhand robot across \textbf{6} real-world tasks, achieving an average transfer ratio of 82.4\%. Finally, we demonstrate that \CEI can also be extended with spatial generalization and multimodal motion generation capabilities using our proposed techniques. Project website: https://cross-embodiment-interface.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
